Transferring policies trained in simulation to real-world deployment.
Jiaxin Shi, Xidong Zhang, Fucai Zhu et al.
We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during action prediction.
Octi Zhang, Mateo Guaman Castro, Patrick Yin et al.
General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation.
Gustavo Claudio Karl Couto, Eric Aislan Antonelo, Gabriel George Zipperer
This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with miniature Ackermann vehicles.
Mathilde Kappel, Clémence Grislain, Mohamed Chetouani et al.
Vision-Language-Action (VLA) models have become a prominent paradigm for mapping multimodal inputs, including semantic instructions, visual observations of the scene, and proprioceptive observations, to robot actions.
Jiahe Pan, Stelian Coros, Jitendra Malik et al.
A primary bottleneck in contact-rich manipulation is the difficulty of collecting real-world data. Sim-to-real reinforcement learning offers a scalable alternative, but the simulation-reality gap prev
Xue Qin, Simin Luan, Cong Yang et al.
Sim-to-real research pursues physics fidelity as a primary objective: simulators are judged by how closely they reproduce real-world contact dynamics.
Hu, Jun, Chen, Sihan, Jovanovic, Kosta et al.
Most robots manipulate objects solely with their end effectors, whereas humans flexibly leverage different body parts, such as the forearm and elbow, especially when handling oversized objects.
Shaocheng Liu, Yuan Meng, Zhenshan Bing et al.
Learning a collective policy for state-based Cartesian manipulation across diverse robotic embodiments presents a fundamental challenge due to embodiment-dependent transition dynamics.
Jusuk Lee, Sungha Kim, Yeonsoo Park et al.
While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions.
Botao He, Yiming Dou, Yiannis Aloimonos et al.
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation?
Devin Han, Hershraj Niranjani
Flow-based policies such as $\pi_0$ are trained on a fixed set of embodiments, and transferring them to new ones is an open problem.
Arman Akbari, Ci Zhang, Arash Akbari et al.
World-action models (WAMs) jointly generate future video and robot actions through iterative diffusion, achieving strong performance on manipulation benchmarks but requiring tens of denoising steps, a cost that precludes real-time control.
Khizra Sohail, Miren Illarramendi, Aitor Arrieta
**Context:** Deep Neural Networks (DNNs) increasingly control Cyber-Physical Systems (CPSs), yet small input perturbations can cause unsafe system-level behavior.
Luca Zanatta, Grzegorz Malczyk, Kostas Alexis
World models, learned generative models that predict how an environment evolves, have become a promising tool for sample-efficient robot learning. Yet how robust they are to environmental variability remains poorly understood.
Tianyi Xie, Haotian Zhang, Jinhyung Park et al.
Scaling humanoid loco-manipulation requires robot-compatible demonstrations across diverse objects, whole-body motions, and scene geometries, but teleoperation and motion capture are difficult to scale because each collection depends on physical setups, instrumented actors, and robot operation.
Wun Lam Yeung, Wenjun Liu, Yui Cheung Yu et al.
Bimanual object reorientation - picking an object, handing it over between two arms, and placing it in a desired target pose - is valuable when direct placement from the initial grasp is infeasible due to collisions, kinematic constraints, or poor final orientation.
Sangwoo Shin, Ashvin Anilkumar, Ryan Gao et al.
Sampling-based system identification estimates physically meaningful parameters by tuning a simulator to reproduce the target system dynamics, providing an interpretable approach to improving sim-to-real transfer.
Siheng Zhao, Yuanhang Zhang, Ziqi Lu et al.
Humanoid robots hold great promise for operating in human-centered environments, yet ladder climbing remains one of the most challenging tasks due to sparse footholds and handholds, complex whole-body coordination, and sensitivity to perception and control errors.
Zuxing Lu, Ziang Zheng, Yao Lyu et al.
Building a general-purpose whole-body controller is essential for enabling diverse motion capabilities in humanoid robots across a wide range of downstream tasks, including locomotion and loco-manipulation.
Ke Zhang, Danica J. Sutherland, Chao Liu
Training data variation, whether through designing a domain randomization (DR) scheme in simulation or curating demonstrations for imitation learning, is a primary lever for improving the robustness of robotic manipulation policies.
Tara Sadjadpour, Siming He, C. K. Wolfe et al.
Human hand-object interactions (HOIs) provide a rich source of demonstrations for dexterous manipulation, but learning directly from them presents challenges in bridging morphology gaps, ensuring dynamical feasibility, and sim-to-real deployment.
Seongjin Bien, D\'ebora Oliveira Makowski, Roberto Calandra et al.
Large-scale datasets are essential for training generalist robot control policies. Collecting real-world tactile data is costly and time-consuming, motivating the use of tactile simulations. However, current tactile simulators capture only overall contact geometry and miss fine details like texture.
Pranay Dugar, Mohitvishnu S. Gadde, Rajesh Mangannavar et al.
Recent progress in learned whole-body controllers for humanoid robots has enabled robust free-space motion by tracking kinematic targets such as joint or end-effector trajectories.
Teodor Ilie, Kleber Cabral, Sidney Givigi
Vehicles that lose control at the handling limit through icing, hydroplaning, or an abrupt evasive maneuver fall outside the operating envelope of production safety systems such as electronic stability control, which focus on preventing instability rather than recovering from it.
Linan Deng, Xing Liu, Lin Hong et al.
Dexterous in-hand manipulation requires coordinated control of multiple actuated joints, and a runtime joint fault can abruptly disrupt the contact configuration required for successful manipulation.
Yishu Li, Liyuan Geng, Xinyi Mao et al.
While pretrained robotic policies exhibit impressive capabilities in controlled environments, unobserved physical properties and dynamics require these policies to rapidly adapt during deployment.
Al Bashir, Partho Ghose, Yibin Wang et al.
Robotic harvesting requires precise manipulation to ensure accurate positioning of the end-effector, especially in a constraint workspace.
He Zhu, Lusen Zhao, Kwan Man Cheng et al.
Large-scale demonstrations have driven unprecedented progress in robot learning, yet collecting robot data through teleoperation is expensive and difficult to scale to diverse environments and long-horizon tasks.
Tao Huang, Ruofei Liu, Xuchen Tang et al.
Humanoid robots have recently demonstrated promising capabilities in real-world ball sports. However, achieving professional motion styles while maintaining strong task performance remains challenging.
Xincheng Tang, Yiji Chen, Youhan Xie et al.
Door opening and traversal is a long-horizon loco-manipulation task that requires precise handle interaction and coordinated base-arm control. We present Video2DoorTraversal, a single-video real-to-sim-to-real framework for wheel-legged mobile manipulators.
Yecheng Jason Ma, William Liang, Guanzhi Wang et al.
Eureka uses GPT-4 to write reward functions for RL environments, achieving human-level reward design on 29 tasks and enabling zero-shot sim-to-real transfer on Shadow Hand dexterous manipulation.
Xuanlin Li, Kyle Hsu, Jiayuan Gu et al.
A benchmark environment for evaluating real-world robot manipulation policies in simulation using real-world video prompts, enabling zero-shot sim-to-real transfer evaluation without physical hardware.
Farama Foundation, Jordan Terry, Mark Towers et al.
Gymnasium is the maintained successor to OpenAI Gym, providing a standardized API for RL environments with 100+ built-in tasks, vectorized parallel execution, and native support for physics engines like MuJoCo, PyBullet, and IsaacGym.
Jiayuan Gu, Sean Xiang, Stone Tao et al.
ManiSkill is a GPU-parallelized robotics simulation benchmark with 20+ manipulation tasks, PartNet-Mobility assets, and unified observation/action spaces, supporting RL, IL, and VLA training at millions of steps per hour.
Bokui Shen, Fei Xia, Chengshu Li et al.
iGibson is a photorealistic, physics-based indoor simulation platform with 15 interactive scenes, 500+ object categories, and GPU-accelerated rendering, enabling RL and VLA training for mobile manipulation and embodied AI.
Read these papers in order to build expertise in Sim-to-Real Transfer.
Jiaxin Shi, Xidong Zhang et al. · 2026
Octi Zhang, Mateo Guaman Castro et al. · 2026
Gustavo Claudio Karl Couto, Eric Aislan Antonelo et al. · 2026
Mathilde Kappel, Clémence Grislain et al. · 2026
Jiahe Pan, Stelian Coros et al. · 2026
Xue Qin, Simin Luan et al. · 2026
…and 29 more papers.
